Regime identification for stratified wakes from limited measurements using a library-based sparse regression formulation

Bluff body wakes in stratified fluids are known to exhibit a rich variety of dynamic behavior that can be categorized into different dynamic regimes based on Reynolds number (Re) and Froude number (Fr). In this work, we attempt to identify the dynamic regime from limited measurement data in a stratified wake with (nominally) unknown Re and Fr. A large database of candidate basis functions is compiled by pooling the DMD modes obtained in prior DNS. A sparse model is built using the Forward Regression with Orthogonal Least Squares (FROLS) algorithm, which sequentially identifies DMD modes that best represent the data and calibrates their amplitude and phase. After calibration, the velocity field can be reconstructed using a weighted combination of the dominant DMD modes. The dynamic regime for the measurements is estimated via a projection-weighted average of Re and Fr corresponding to the identified modes. Regime identification is carried out from a limited number of 2D velocity snapshots from numerical and experimental datasets, as well as 3 point measurements in the wake of the body. A metric to assess confidence is introduced based on the observed predictive capability. This approach holds promise for the implementation of data-driven fluid pattern classifiers.